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Quality of Care and Relative Resource Use for Patients With Diabetes
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Quality of Care and Relative Resource Use for Patients With Diabetes

Troy Quast, PhD
Quality of care and relative resource use for patients with diabetes are not necessarily positively related. Further, the relationship varies by year, plan type, and region.
The third layer of disaggregation is detailed in Table 2, where the results are reported by geographic region. (Estimates are reported only for regions with 50 or more observations.) Even though splitting the sample by region leads to relatively small samples, there are a number of significant findings. Chicago and New York appear to be the primary drivers for the negative relationship between quality and the total medical RRU measure. In both regions there is a relatively large negative association between quality and the inpatient aspect of evaluation and management services measure and the inpatient facility measure. The pattern of negative correlations for this measure persists across all regions. By contrast, there is a positive association in Atlanta and Boston for the outpatient aspect of procedure and surgery services.

First Differences

While the findings in Table 1 and Table 2 provide insight into how the levels of quality and RRU are related, they do not indicate how changes in quality and RRU are related. Although plans differ significantly, correlations of first differences can provide insight into the relationship between quality and cost within plans. Since differencing abstracts from the initial quality and costs levels, any differences in plan attributes, such as population, that may affect quality and costs and do not vary during the sample period will be at least partially controlled for.

Table 3 presents the correlations of first differences. Because first differences are examined, the sample sizes are significantly lower and results are only available for the last 2 years of the sample period. Further, the observations are limited to those plans that submitted consecutive annual reports. Correlations by region are not reported because of the small samples due to first differencing.

An important limitation to this analysis is that the RRU measures are indexed based on the group of plans that submit in that year. Thus, a plan could have a higher or lower index value due to changes in the composition of plans that submitted data in that year. Further, the RRU measures are calculated using a ratio that that is year-specific. Given these factors, the correlations should be interpreted as describing the relationship between changes in quality and changes in the relative standing in RRU.

The overall results indicate a negative association between changes in quality and (changes in relative standing in) the total medical RRU measure. The subcategory results show that this negative relationship is driven by the inpatient measures in each subcategory and often by the inpatient facility measure. The findings by year show that while the negative total medical association is due largely to changes observed in 2010, the pattern of correlations is largely consistent across both years.

By contrast, the estimates differ significantly across plan types. While there is a negative association between quality and total medical for HMO/POS and PPO, there is virtually no association for HMO. Also, while the negative relationship for PPO is driven by both inpatient measures, for HMO/POS the negative correlation is due largely to the outpatient aspect of evaluation and management services.


The correlations presented above based on the full sample roughly follow those found in earlier analyses of the relationship between quality and relative resource use in diabetes.7 There is a generally negative relationship between quality and total medical services. However, the negative association is stronger for the inpatient facility measure and the inpatient facility components of procedure and surgery and evaluation and management. There is a generally positive relationship with total ambulatory pharmacy.

However, the disaggregated and first difference estimates provide greater detail than previous studies do. For instance, the relationship between quality and the RRU measures appears to vary over time. Also, while quality and total evaluation and management services were negatively correlated for the HMO and HMO/POS samples, the correlation was positive for the PPO sample. Finally, the analysis of first differences suggests that changes in quality and RRU are strongly negatively related, driven largely by the inpatient RRU measures.


There are several important limitations to this analysis. Arguably the most significant is the inability to go beyond correlations and estimate the causal relationship between quality and cost. The effects of unobserved confounders may bias the estimated associations away from the true effects of cost on quality. However, as mentioned above, the first differences estimates may somewhat mitigate this concern.

Another impediment to discerning the causal relationship is ambiguity in the directional relationship: Does quality affect cost or does cost affect quality? While the simple correlations presented above cannot provide insight into this issue, a possible framework in which to view the relationship is to consider a simple health production function20 where medical spending has a positive effect on health up to the “flat of the curve.” After this point, increased spending does not affect health. In the present context, the quality measure may act as a proxy for health, especially given the nonprocess measures included in the index. Nevertheless, this proposed framework is speculative and this study cannot speak to this important question.

Further, as noted above, the statistical significance of the results should be interpreted with caution. The number of comparisons for each RRU measure implies that there is a considerable probability that some of the findings of a statistically significant correlation are spurious. Thus, the number of findings of a statistical significance could be interpreted as an upper bound.

Also, the RRU measures are indexed values rather than actual costs. Thus, for instance, a plan’s costs may increase yet the indexed value may fall. However, while the indexed values may not reflect the actual absolute change in costs, they may provide important insight. For instance, the relative ranking controls for health cost inflation across all plans and does not penalize plans whose costs follow overall trends. Also, given that the correlations estimated above are intended to measure directional associations, relative rankings are well suited for the analysis. Finally, the rankings reflect important adjustments to account for case-mix differences across plans.

Finally, the use of plan-level data abstracts significantly from individual conditions and choices. For instance, while the severity of diabetes is closely related to an individual’s medical costs,21 the data employed here do not contain any information regarding severity. Thus, it is not possible to distinguish between a plan incurring potentially wasteful costs and a plan with high costs due to serving a large proportion of patients with advanced diabetics. While first differences may somewhat address these concerns, the analysis would be improved by incorporating this information and estimating the effects of these characteristics.


Despite these caveats, the results in this paper suggest potentially useful implications. The correlations suggest that quality and RRU are not necessarily positively related. The especially negative association for the inpatient measures could indicate that some of these resources may not be efficiently utilized. Alternatively, they could reflect that patients with lower quality of care are more likely to require hospitalizations due to their diabetes not being properly treated. The generally positive correlations for ambulatory pharmacy are also consistent with this interpretation, in that patients who stay current with their medications are more likely to have their LDL-C and blood pressure under control.

The first difference results further indicate that changes in quality and RRU are also not necessarily positively related. Thus, there may be “low-hanging fruit” scenario, in that increases in quality may be attainable without increases in resource use. The finding of regional differences in the relationship may also provide insight into the geographic variation in healthcare utilization. For instance, the negative relationship between quality and total medical in Chicago and New York may reflect earlier findings that geographic variation is due in part to levels of insurance coverage and wealth.22

Future Research

There are numerous potential extensions to the analyses in this paper. Arguably the most important would be to attempt to identify causal relationships. Given random assignment does not appear to be feasible, an instrumental variables approach could be used to go beyond the associations described here. Another important extension would be to analyze conditions other than diabetes, as the associations observed for diabetes necessarily may not necessarily hold for other conditions. Finally, there is an inherent tension in analyzing costs for chronic conditions, as improved medical care implies patients live longer, which in turn increases the costs to treat these conditions. Measures of indirect costs could potentially provide improved insight into truly wasteful costs.


The findings suggest, given that quality and RRU are not necessarily positively related, that it may be possible to improve diabetes care quality at a relatively low cost. However, the disaggregated results suggest that the potential to increase quality can vary significantly by resource type, plan type, and geographic region. The analysis of first differences further indicates that quality and certain RRU measures may not be positive related. Thus, the most effective policies to improve quality may vary by plan and type of care.


The author would like to thank Robert Saunders, PhD, of NCQA for helpful comments and suggestions regarding this manuscript.

 Author Affiliation: University of South Florida, College of Public Health, Tampa, FL.

Source of Funding: This manuscript was funded by a Faculty Research Grant awarded by the Sam Houston State University College of Business.

Author Disclosures: Dr Quast reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. During the preparation of this manuscript, Dr Quast was affiliated with Sam Houston State University, Huntsville, TX.

Authorship Information: Concept and design; acquisition of data; analysis and interpretation of data; drafting of the manuscript; critical revision of the manuscript for important intellectual content; statistical analysis; provision of patients or study materials.

Address correspondence to: Troy Quast, PhD, University of South Florida, Department of Health Policy and Management, 12901 Bruce B. Downs Blvd, Tampa, FL 33612. E-mail:
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